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This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and model robustness. We introduce supervised contrastive active learning by leveraging the contrastive loss for a
Externí odkaz:
http://arxiv.org/abs/2109.06321
We introduce supervised contrastive active learning (SCAL) and propose efficient query strategies in active learning based on the feature similarity (featuresim) and principal component analysis based feature-reconstruction error (fre) to select info
Externí odkaz:
http://arxiv.org/abs/2109.06873
Publikováno v:
In Journal of Marine Systems May 2024 244
Autor:
Panthri, Medha, Saini, Himanshu, Banerjee, Gopal, Bhatia, Priyanka, Verma, Neetu, Sinha, Alok Krishna, Gupta, Meetu
Publikováno v:
In Journal of Hazardous Materials 5 April 2024 467